Joint components based pedestrian detection in crowded scenes using extended feature descriptors

Abstract

This paper presents a framework for human detection based on the joint component model using extended feature descriptors. This framework provides two contributions for handling the partially occluded problem of pedestrian detection in crowded environments. First, it presents feature descriptors based on extension of two well-known feature descriptors for accommodation in partially occluded pedestrian detection. The feature descriptors, which use multiple scale blocks-based histograms of oriented gradients (MHOG) and parallelogram based Haar-like feature (PHF), are proposed for improving the accuracy of the detection system. As a result of using MHOG, an extensive feature space allows for the obtaining of highly discriminated features, which supports robust detection. On the other hand, the PHF is adaptive for the shape of human limbs in detection of pedestrians. This contribution also presents special data structures to store image intensities and image gradients for using an integral image method, which is helpful for fast computation for both feature descriptors. Second, the joint component model for human detection is proposed for the training and detecting of pedestrians in crowded scenes. The proposed detection method based on fusion of the boosting technique and the support vector machine (SVM) is presented. The SVM is known as one of the most efficient learning models for classification. The advantage of boosting is a strong classifier based on a combination set of weak classifiers. However, the performance of boosting depends on the kernel of element classifier. The method based on the extended feature descriptors and the joint component model is helpful for constructing an efficient classification. The experimental results demonstrate that this method for pedestrian detection outperforms current methods in crowded situations under a variety of outdoor environments.